A Discourse Analysis of Canadian PM’s Speech after New Zealand Christchurch Mosque Shootings
Bibliographic record
Abstract
We use language for different purposes that are mostly related to social practices in different contexts and perspectives. Discourse analysis is one of the disciplines which examines the use of language from different perspectives to reach a possible understanding of the discourse. This paper is also an attempt to analyze the language used in a particular context and perspective to understand and expose some constructed realities. The objective of this study is to examine the Canadian PM’s moral and ideological standpoint, his commitment to show solidarity with the grieved community, his determination to eradicate terrorism and his linguistic characterization of terrorism that he confirmed in his speech in the House of Common on March 18, 2019, after the Christchurch Mosque Shootings in New Zealand. The analysis is based on Fairclough’s conceptions in CDA. It claims that ideologies and texts are interrelated, and it is not possible to break this link between ideologies and texts because the texts can be interpreted in maximum possible ways. This study analyzes the components of ideology and persuasion used in Justin Trudeau’s speech to reveal his commitment and persuasive strategies against terrorism, and it gives new hopes to the targeted communities worldwide as well as the general public. He tried to ensure the public that they are not alone because the world leaders and the heads of the states are unconditionally united to eradicate worldwide terrorism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".